基于无人机的视觉惯性导航系统的适应性共变矩阵使用高斯式公式
Yangzi Cong1, Wenbin Su2, Nan Jiang1
1Institute of Space Sciences, Shandong University, Weihai 264209, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了无人机视觉惯性导航系统 (VINS) 的自适应共变矩阵方法. 这种方法提高了无人机导航的准确性和稳定性,在具有挑战性的条件下,如运动模糊.
科学领域:
- 机器人技术和自主系统
- 计算机视觉 计算机视觉
- 导航系统 导航系统
背景情况:
- 视觉惯性导航系统 (VINS) 对于无人机 (UAV) 应用至关重要.
- 传统的VINS在适应环境变化方面面临限制,原因是固定的协差矩阵.
- 高速无人机操作,动作模糊和图像清晰度差,降低了VINS的准确性和稳定性.
研究的目的:
- 为基于无人机的VINS.开发一种适应性协差矩阵估计方法.
- 在不同的图像质量条件下提高导航精度和系统稳定性.
- 为了应对高速无人机导航中运动模糊所带来的挑战.
主要方法:
- 提出了一种适应性共变矩阵估计方法,使用高斯式公式用于基于无人机的VINS.
- 使用拉普拉斯运算符对图像模糊和质量的详细评估.
- 实现了一种基于图像清晰度动态调整视觉协差矩阵的新机制.
主要成果:
- 拟议的方法在动作模糊的场景中显著改善了无人机导航准确度.
- 与VINS-Mono框架相比,实现了更高的准确性,平均表现比VINS-Mono框架高18.18%.
- 在实地测试中对根平均平方 (RMS) 错误进行了实质性的优化率 (F1 的 65.66%,F2 的 41.74%).
结论:
- 适应性共变矩阵估计方法提高了基于无人机的VINS的准确性和稳定性.
- 该方法有效地减轻了动作模糊和图像清晰度波动的负面影响.
- 通过广泛的模拟和现场测试来验证,证明其实际适用性.
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